How to run cor.test with nested loops - r

Question

I am trying to run a correlation test on different data frames representing the number of unique stores an employee is assigned and columns repenting different regions simultaneously. My data frame is split by the number of unique stores each employee has by:
unique_store_breakdown <- split(Data, as.factor(Data$unique_stores))
Ideally I would like the output:
Region -- unique_store -- correlation
Midwest ------- 1 -------------- .05
Midwest ------- 2 -------------- .04
.
.
Southeast ----- 1 ------------- 0.75
.
.
cor_tests <-list()
counter = 0
for (i in unique(j$region)){
for (j in 1: length(unique_store_breakdown)){
counter = counter + 1
#Create new variables for correlation test
x = as.numeric(j[j$region == i,]$quality)
y = as.numeric(j[j$region == i,]$rsv)
cor_tests[[counter]] <- cor.test(x,y)
}}
cor_tests
I am able to run this for one dataframe at a time, but when I try to add the nested loop (j term) I receive the error "Error: $ operator is invalid for atomic vectors. Additionally I would also like to output the results as a dataframe rather than a list if possible.

Answer 1

If all you want to do is perform cor.test() for each store, it should be fairly simple using by(). The output from by() is a regular list, it's just the printing that is a little special.
# example data
set.seed(1)
dtf <- data.frame(store=rep(1:3, each=30), rsv=rnorm(90))
dtf$quality <- dtf$rsv + rnorm(90, 0, dtf$store)
# perform cor.test for every store
by(dtf, dtf$store, function(x) cor.test(x$quality, x$rsv))
# dtf$store: 1
#
# Pearson's product-moment correlation
#
# data: x$quality and x$rsv
# t = 5.5485, df = 28, p-value = 6.208e-06
# alternative hypothesis: true correlation is not equal to 0
# 95 percent confidence interval:
# 0.4915547 0.8597796
# sample estimates:
# cor
# 0.7236681
#
# ------------------------------------------------------------------------------
# dtf$store: 2
#
# Pearson's product-moment correlation
#
# data: x$quality and x$rsv
# t = 0.68014, df = 28, p-value = 0.502
# alternative hypothesis: true correlation is not equal to 0
# 95 percent confidence interval:
# -0.2439893 0.4663368
# sample estimates:
# cor
# 0.1274862
#
# ------------------------------------------------------------------------------
# dtf$store: 3
#
# Pearson's product-moment correlation
#
# data: x$quality and x$rsv
# t = 2.2899, df = 28, p-value = 0.02977
# alternative hypothesis: true correlation is not equal to 0
# 95 percent confidence interval:
# 0.04304952 0.66261810
# sample estimates:
# cor
# 0.397159
#